Applying Artificial Neural Network and Response Surface Method to Forecast the Rheological Behavior of Hybrid Nano-Antifreeze Containing Graphene Oxide and Copper Oxide Nanomaterials

Ammar A. Melaibari, Yacine Khetib, Abdullah K. Alanazi, S. Mohammad Sajadi, Mohsen Sharifpur and Goshtasp Cheraghian
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Ammar A. Melaibari: Mechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah 80204, Saudi Arabia
Yacine Khetib: Mechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah 80204, Saudi Arabia
Abdullah K. Alanazi: Department of Chemistry, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
S. Mohammad Sajadi: Department of Nutrition, Cihan University-Erbil, Kurdistan Region, Erbil 44001, Iraq
Mohsen Sharifpur: Department of Mechanical and Aeronautical Engineering, University of Pretoria, Pretoria 0002, South Africa
Goshtasp Cheraghian: Independent Researcher, 38106 Braunschweig, Germany

Sustainability, 2021, vol. 13, issue 20, 1-17

Abstract: In this study, the efficacy of loading graphene oxide and copper oxide nanoparticles into ethylene glycol-water on viscosity was assessed by applying two numerical techniques. The first technique employed the response surface methodology based on the design of experiments, while in the second technique, artificial intelligence algorithms were implemented to estimate the GO-CuO/water-EG hybrid nanofluid viscosity. The nanofluid sample’s behavior at 0.1, 0.2, and 0.4 vol.% is in agreement with the Newtonian behavior of the base fluid, but loading more nanoparticles conforms with the behavior of the fluid with non-Newtonian classification. Considering the possibility of non-Newtonian behavior of nanofluid temperature, shear rate and volume fraction were effective on the target variable and were defined in the implementation of both techniques. Considering two constraints (i.e., the maximum R-square value and the minimum mean square error), the best neural network and suitable polynomial were selected. Finally, a comparison was made between the two techniques to evaluate their potential in viscosity estimation. Statistical considerations proved that the R-squared for ANN and RSM techniques could reach 0.995 and 0.944, respectively, which is an indication of the superiority of the ANN technique to the RSM one.

Keywords: hybrid nanofluid; viscosity; arterial neural network; response surface method (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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